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Climate models predict an increased likelihood of seasonal droughts for many areas of the world. Breeding for drought tolerance could be accelerated by marker-assisted selection. As a basis for marker identification, we studied the genetic variance, predictability of field performance and potential costs of tolerance in potato (Solanum tuberosum L.). Potato produces high calories per unit of water invested, but is drought-sensitive. In 14 independent pot or field trials, 34 potato cultivars were grown under optimal and reduced water supply to determine starch yield. In an artificial dataset, we tested several stress indices for their power to distinguish tolerant and sensitive genotypes independent of their yield potential. We identified the deviation of relative starch yield from the experimental median (DRYM) as the most efficient index. DRYM corresponded qualitatively to the partial least square model-based metric of drought stress tolerance in a stress effect model. The DRYM identified significant tolerance variation in the European potato cultivar population to allow tolerance breeding and marker identification. Tolerance results from pot trials correlated with those from field trials but predicted field performance worse than field growth parameters. Drought tolerance correlated negatively with yield under optimal conditions in the field. The distribution of yield data versus DRYM indicated that tolerance can be combined with average yield potentials, thus circumventing potential yield penalties in tolerance breeding.

Systems responses to drought stress of four potato reference cultivars with differential drought tolerance (Solanum tuberosum L.) were investigated by metabolome profiling and RNA sequencing. Systems analysis was based on independent field and greenhouse trials. Robust differential drought responses across all cultivars under both conditions comprised changes of proline, raffinose, galactinol, arabitol, arabinonic acid, chlorogenic acid and 102 transcript levels. The encoded genes contained a high proportion of heat shock proteins and proteins with signalling or regulatory functions, for example, a homolog of abscisic acid receptor PYL4. Constitutive differences of the tolerant compared with the sensitive cultivars included arbutin, octopamine, ribitol and 248 transcripts. The gene products of many of these transcripts were pathogen response related, such as receptor kinases, or regulatory proteins, for example, a homolog of the Arabidopsis FOUR LIPS MYB-regulator of stomatal cell proliferation. Functional enrichment analyses imply heat stress as a major acclimation component of potato leaves to long-term drought stress. Enhanced heat stress during drought can be caused by loss of transpiration cooling. This effect and CO limitation are the main consequences of drought-induced or abscisic acid-induced stomatal closure. Constitutive differences in metabolite and transcript levels between tolerant and sensitive cultivars indicate interactions of drought tolerance and pathogen resistance in potato.

SummaryPotato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker‐assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT‐PCR and GC‐MS profiling, respectively. Transcript marker candidates were selected from a published RNA‐Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6% and 9%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions.